Method for three-dimensional human pose estimation

Abstract

The invention discloses a method for three-dimensional human pose estimation, which can realize the real-time and high-precision 3D human pose estimation without high configuration hardware support and precise human body model. In this method for three-dimensional human pose estimation, including the following steps: (1) establishing a three-dimensional human body model matching the object, which is a cloud point human body model of visible spherical distribution constraint. (2) Matching and optimizing between human body model for human body pose tracking and depth point cloud. (3) Recovering for pose tracking error based on dynamic database retrieval.

Description

CROSS REFERENCE TO THE RELATED APPLICATIONS[0001]This application is based upon and claims priority to Chinese Patent Application No. CN 201910201559.3, filed on Mar. 18, 2019, the entire contents of which are incorporated herein by reference.TECHNICAL FIELD[0002]The present invention relates to the technical field of computer vision and pattern recognition, and particularly to a method for three-dimensional human pose estimation.BACKGROUND OF THE INVENTION[0003]Three-dimensional (3D) human pose estimation based on computer vision technology has been widely used in many fields of human life, such as computer animation, medicine, human-computer interaction and so on. With the introduction of low-cost RGB-D sensors (such as Kinect), compared with RGB visual information, depth image can greatly avoid data defects caused by complex background and changes in light conditions. Therefore, the performance of 3D human pose estimation is improved obviously by using the depth information, whic...

AI Technical Summary

Benefits of technology

The technical effect of this patent is to provide a method for accurately estimating the position of a person's body in real-time, without the need for expensive special hardware or a detailed body model.

Problems solved by technology

The former relies on a large number of training data, and therefore can adapt to the changes of different body types, but most of them can not get higher precision in the case of complex motion; the latter usually depends on complex and accurate human body model, and therefore can get high precision in the case of data loss, but in the case of fast and complex motion, it is easy to fall into local optimization and lose the global optimum solution.

These limitations limit the application of real-time human-computer interaction on the platform of general hardware configuration.

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